1. Field of the Invention
The invention relates to a nuclear detector (gamma camera) for nuclear medicine imaging and the use of a wavelet based neural network (WNN) for segmenting noise from signal and the restoration of the signal using the neural network.
2. Description of the Related Art
The gamma camera system response function for single photon detection results in image degradation due to photon scattering effects or photon penetration through the collimated detector [1]. Alternatively for bremsstrahlung imaging, such as in the detection of beta particles during antibody therapy, the enhanced effects of photon scattering and high energy photon penetration through the collimated detector results in further significant image degradation [2]. The sensitivity of detection of beta particles, as opposed to single photon detection, is greatly diminished due to the poor conversion efficiency of beta particle energy to bremsstrahlung radiation and results in poor signal to noise ratio in bremsstrahlung detection[2]. Quantitative imaging of bremsstrahlung radiation, therefore, poses a very difficult problem for image restoration; namely the detection of weak signals imbedded in noise, where the Poisson noise processes includes both additive and multiplicative sources [2-3]. Similarly, the conditions for image restoration filters must be met for bremsstrahlung detection namely: the shift invariance of the point spread function (PSF), radial symmetry of the PSF and finally uniformity of the PSF with source depth [3]. These conditions have been achieved using pure beta emitters .sup.32 P[3] and .sup.90 Y[4] as reported by these investigators.
Image restoration filters have been proposed for single photon detection to partly compensate for image degradation and have met with limited success. Methods proposed have included the Weiner filter [4], the Metz filter and constrained Least Squares filters [5] that generally require an a priori knowledge of the system response function and an estimate of the noise power spectrum. These filters generally involve two components, a low pass filter for noise suppression and an inverse filter for deconvolution. These filters have had limited success because the inverse operation often makes the restoration an ill conditioned, unstable, or singular problem. When the Weiner filter is applied to bremsstrahlung images, the combined influence of the degraded system response and high noise content introduces greater instability in the deconvolution that results in ringing artifacts and an over compensation of the system response function [5, 6]. These ringing artifacts and restoration instability are particularly noticeable for images of isolated sources used to calibrate the gamma camera for quantitative measurements as proposed in this work [7]. Alternative methods for image restoration are therefore necessary for imaging of beta emitters using the gamma camera for either the planar or tomographic detection modes. Similar alternative methods are required for imaging of single photon emitters (gamma rays) or positrons using the gamma camera in either the planar or tomographic detector modes.
The prior art proposed the use of a novel order statistic neural network hybrid (OSNNH) filter for image restoration for bremsstrahlung detection [6]. The order statistic filter was used for noise suppression for the first stage, cascaded with the neural network (NN) for deconvolution. Order statistic filters have been shown to provide a more robust performance than linear filters or modification to median filters for noise suppression [7] and performed well for noise suppression in bremsstrahlung imaging [6]. The NN system for deconvolution that includes a knowledge of the system response function, in turn, avoided the serious problem of the inverse filter, since for this algorithm no matrices are inverted, as previously reported [8-10]. The OSNNH filter, however, may not have optimum performance for all image noise levels. Furthermore, it is computationally intensive and the overall filter architecture is not optimum for either parallel processing or very large system integration (VLSI) implementation as required for planar or SPECT modes of detection for the gamma camera.
For the above cited and other related articles, see the references listed at the end of the specification.